Overview

Dataset statistics

Number of variables11
Number of observations5763
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory540.3 KiB
Average record size in memory96.0 B

Variable types

Numeric11

Alerts

gross_revenue is highly overall correlated with total_invoices and 3 other fieldsHigh correlation
recency_days is highly overall correlated with total_invoicesHigh correlation
frequency is highly overall correlated with total_invoices and 1 other fieldsHigh correlation
total_invoices is highly overall correlated with gross_revenue and 5 other fieldsHigh correlation
items_qtt is highly overall correlated with gross_revenue and 4 other fieldsHigh correlation
products_qtt is highly overall correlated with gross_revenue and 4 other fieldsHigh correlation
avg_basket_size is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_unique_basket_size is highly overall correlated with products_qtt and 1 other fieldsHigh correlation
returns_qtt is highly overall correlated with total_invoicesHigh correlation
gross_revenue is highly skewed (γ1 = 21.44925486)Skewed
avg_ticket is highly skewed (γ1 = 53.23323582)Skewed
items_qtt is highly skewed (γ1 = 23.13817362)Skewed
avg_basket_size is highly skewed (γ1 = 49.08815403)Skewed
returns_qtt is highly skewed (γ1 = 52.29085591)Skewed
customer_id has unique valuesUnique
returns_qtt has 4207 (73.0%) zerosZeros

Reproduction

Analysis started2023-09-12 00:19:49.987264
Analysis finished2023-09-12 00:20:28.282134
Duration38.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

UNIQUE 

Distinct5763
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16646.304
Minimum12346
Maximum22709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-09-11T21:20:28.597140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12702.1
Q114306.5
median16271
Q318262.5
95-th percentile21778.9
Maximum22709
Range10363
Interquartile range (IQR)3956

Descriptive statistics

Standard deviation2833.4858
Coefficient of variation (CV)0.17021711
Kurtosis-0.86084354
Mean16646.304
Median Absolute Deviation (MAD)1978
Skewness0.42501083
Sum95932651
Variance8028641.8
MonotonicityNot monotonic
2023-09-11T21:20:29.141281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
21107 1
 
< 0.1%
17899 1
 
< 0.1%
13833 1
 
< 0.1%
17312 1
 
< 0.1%
17939 1
 
< 0.1%
14053 1
 
< 0.1%
16009 1
 
< 0.1%
12922 1
 
< 0.1%
21097 1
 
< 0.1%
Other values (5753) 5753
99.8%
ValueCountFrequency (%)
12346 1
< 0.1%
12347 1
< 0.1%
12348 1
< 0.1%
12349 1
< 0.1%
12350 1
< 0.1%
12352 1
< 0.1%
12353 1
< 0.1%
12354 1
< 0.1%
12355 1
< 0.1%
12356 1
< 0.1%
ValueCountFrequency (%)
22709 1
< 0.1%
22708 1
< 0.1%
22707 1
< 0.1%
22706 1
< 0.1%
22705 1
< 0.1%
22704 1
< 0.1%
22700 1
< 0.1%
22699 1
< 0.1%
22696 1
< 0.1%
22695 1
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5517
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1846.5836
Minimum0.42
Maximum280206.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-09-11T21:20:29.625107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile12.45
Q1240.87
median632.4
Q31640.115
95-th percentile5490.526
Maximum280206.02
Range280205.6
Interquartile range (IQR)1399.245

Descriptive statistics

Standard deviation7918.6308
Coefficient of variation (CV)4.2882601
Kurtosis600.90765
Mean1846.5836
Median Absolute Deviation (MAD)497.6
Skewness21.449255
Sum10641861
Variance62704714
MonotonicityNot monotonic
2023-09-11T21:20:30.143722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.95 9
 
0.2%
2.95 8
 
0.1%
4.95 8
 
0.1%
1.25 8
 
0.1%
12.75 7
 
0.1%
3.75 7
 
0.1%
1.65 7
 
0.1%
4.25 6
 
0.1%
7.5 6
 
0.1%
5.95 6
 
0.1%
Other values (5507) 5691
98.8%
ValueCountFrequency (%)
0.42 1
 
< 0.1%
0.55 1
 
< 0.1%
0.65 1
 
< 0.1%
0.79 1
 
< 0.1%
0.84 3
 
0.1%
0.85 3
 
0.1%
1.07 1
 
< 0.1%
1.1 1
 
< 0.1%
1.25 8
0.1%
1.34 1
 
< 0.1%
ValueCountFrequency (%)
280206.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
143825.06 1
< 0.1%
124914.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
81024.84 1
< 0.1%
77183.6 1
< 0.1%

avg_ticket
Real number (ℝ)

SKEWED 

Distinct5564
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.500718
Minimum0.42
Maximum77183.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-09-11T21:20:30.656134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile3.4123474
Q18.505137
median16.129773
Q322.771186
95-th percentile80.292833
Maximum77183.6
Range77183.18
Interquartile range (IQR)14.266049

Descriptive statistics

Standard deviation1276.6179
Coefficient of variation (CV)21.822261
Kurtosis2961.2007
Mean58.500718
Median Absolute Deviation (MAD)7.2988374
Skewness53.233236
Sum337139.64
Variance1629753.4
MonotonicityNot monotonic
2023-09-11T21:20:31.217406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.75 11
 
0.2%
4.95 10
 
0.2%
2.95 9
 
0.2%
1.25 9
 
0.2%
7.95 8
 
0.1%
1.65 7
 
0.1%
8.25 7
 
0.1%
12.75 7
 
0.1%
3.35 6
 
0.1%
15 6
 
0.1%
Other values (5554) 5683
98.6%
ValueCountFrequency (%)
0.42 2
< 0.1%
0.535 1
 
< 0.1%
0.55 1
 
< 0.1%
0.65 1
 
< 0.1%
0.79 1
 
< 0.1%
0.8371428571 1
 
< 0.1%
0.84 2
< 0.1%
0.85 3
0.1%
1.002222222 1
 
< 0.1%
1.02 1
 
< 0.1%
ValueCountFrequency (%)
77183.6 1
< 0.1%
56157.5 1
< 0.1%
13305.5 1
< 0.1%
4453.43 1
< 0.1%
4287.63 1
< 0.1%
3861 1
< 0.1%
3096 1
< 0.1%
2653.95 1
< 0.1%
2583.76 1
< 0.1%
2033.1 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION 

Distinct304
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.00451
Minimum0
Maximum373
Zeros38
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-09-11T21:20:31.736073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median72
Q3199
95-th percentile337
Maximum373
Range373
Interquartile range (IQR)176

Descriptive statistics

Standard deviation111.29419
Coefficient of variation (CV)0.95119574
Kurtosis-0.6351504
Mean117.00451
Median Absolute Deviation (MAD)62
Skewness0.8132583
Sum674297
Variance12386.397
MonotonicityNot monotonic
2023-09-11T21:20:32.279011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 110
 
1.9%
4 105
 
1.8%
3 99
 
1.7%
2 92
 
1.6%
10 86
 
1.5%
8 82
 
1.4%
9 80
 
1.4%
17 79
 
1.4%
7 78
 
1.4%
15 66
 
1.1%
Other values (294) 4886
84.8%
ValueCountFrequency (%)
0 38
 
0.7%
1 110
1.9%
2 92
1.6%
3 99
1.7%
4 105
1.8%
5 52
0.9%
7 78
1.4%
8 82
1.4%
9 80
1.4%
10 86
1.5%
ValueCountFrequency (%)
373 23
0.4%
372 23
0.4%
371 17
0.3%
369 4
 
0.1%
368 13
0.2%
367 17
0.3%
366 15
0.3%
365 19
0.3%
364 11
0.2%
362 7
 
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION 

Distinct1243
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55110525
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-09-11T21:20:32.721077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.011070111
Q10.025244559
median1
Q31
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.97475544

Descriptive statistics

Standard deviation0.55129662
Coefficient of variation (CV)1.0003473
Kurtosis136.5065
Mean0.55110525
Median Absolute Deviation (MAD)0
Skewness4.7966628
Sum3176.0195
Variance0.30392797
MonotonicityNot monotonic
2023-09-11T21:20:33.072091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2927
50.8%
2 51
 
0.9%
0.0625 18
 
0.3%
0.02777777778 17
 
0.3%
0.02380952381 17
 
0.3%
0.09090909091 15
 
0.3%
0.08333333333 14
 
0.2%
0.02941176471 13
 
0.2%
0.07692307692 13
 
0.2%
0.02127659574 13
 
0.2%
Other values (1233) 2665
46.2%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
< 0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
< 0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
< 0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
4 2
 
< 0.1%
3 4
 
0.1%
2 51
 
0.9%
1.142857143 1
 
< 0.1%
1 2927
50.8%
0.75 1
 
< 0.1%
0.6666666667 4
 
0.1%
0.5588235294 1
 
< 0.1%
0.5388739946 1
 
< 0.1%

total_invoices
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4629533
Minimum1
Maximum209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-09-11T21:20:33.467071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum209
Range208
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.8261859
Coefficient of variation (CV)1.9712035
Kurtosis310.00283
Mean3.4629533
Median Absolute Deviation (MAD)0
Skewness13.354763
Sum19957
Variance46.596814
MonotonicityNot monotonic
2023-09-11T21:20:33.899569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2918
50.6%
2 835
 
14.5%
3 508
 
8.8%
4 388
 
6.7%
5 242
 
4.2%
6 172
 
3.0%
7 143
 
2.5%
8 98
 
1.7%
9 68
 
1.2%
10 54
 
0.9%
Other values (49) 337
 
5.8%
ValueCountFrequency (%)
1 2918
50.6%
2 835
 
14.5%
3 508
 
8.8%
4 388
 
6.7%
5 242
 
4.2%
6 172
 
3.0%
7 143
 
2.5%
8 98
 
1.7%
9 68
 
1.2%
10 54
 
0.9%
ValueCountFrequency (%)
209 1
< 0.1%
201 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
93 1
< 0.1%
91 1
< 0.1%
86 1
< 0.1%
73 1
< 0.1%
63 1
< 0.1%
62 1
< 0.1%

items_qtt
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1857
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean969.6979
Minimum1
Maximum196915
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-09-11T21:20:34.397813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q1103
median312
Q3800
95-th percentile2920.6
Maximum196915
Range196914
Interquartile range (IQR)697

Descriptive statistics

Standard deviation4408.3364
Coefficient of variation (CV)4.5460926
Kurtosis791.99992
Mean969.6979
Median Absolute Deviation (MAD)252
Skewness23.138174
Sum5588369
Variance19433430
MonotonicityNot monotonic
2023-09-11T21:20:34.952167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 164
 
2.8%
2 73
 
1.3%
3 55
 
1.0%
4 51
 
0.9%
5 36
 
0.6%
6 28
 
0.5%
12 25
 
0.4%
88 23
 
0.4%
72 19
 
0.3%
84 19
 
0.3%
Other values (1847) 5270
91.4%
ValueCountFrequency (%)
1 164
2.8%
2 73
1.3%
3 55
 
1.0%
4 51
 
0.9%
5 36
 
0.6%
6 28
 
0.5%
7 19
 
0.3%
8 18
 
0.3%
9 8
 
0.1%
10 17
 
0.3%
ValueCountFrequency (%)
196915 1
< 0.1%
80997 1
< 0.1%
80265 1
< 0.1%
77374 1
< 0.1%
74215 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%

products_qtt
Real number (ℝ)

HIGH CORRELATION 

Distinct526
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.982821
Minimum1
Maximum7847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-09-11T21:20:35.953375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q114
median40
Q3105
95-th percentile332.9
Maximum7847
Range7846
Interquartile range (IQR)91

Descriptive statistics

Standard deviation209.90908
Coefficient of variation (CV)2.2820465
Kurtosis513.85265
Mean91.982821
Median Absolute Deviation (MAD)32
Skewness17.802916
Sum530097
Variance44061.82
MonotonicityNot monotonic
2023-09-11T21:20:36.502636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 300
 
5.2%
2 155
 
2.7%
3 111
 
1.9%
5 96
 
1.7%
10 93
 
1.6%
6 90
 
1.6%
9 89
 
1.5%
11 88
 
1.5%
4 87
 
1.5%
7 86
 
1.5%
Other values (516) 4568
79.3%
ValueCountFrequency (%)
1 300
5.2%
2 155
2.7%
3 111
 
1.9%
4 87
 
1.5%
5 96
 
1.7%
6 90
 
1.6%
7 86
 
1.5%
8 86
 
1.5%
9 89
 
1.5%
10 93
 
1.6%
ValueCountFrequency (%)
7847 1
< 0.1%
5675 1
< 0.1%
5111 1
< 0.1%
4595 1
< 0.1%
2700 1
< 0.1%
2379 1
< 0.1%
2076 1
< 0.1%
1818 1
< 0.1%
1677 1
< 0.1%
1637 1
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2371
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263.77715
Minimum1
Maximum74215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-09-11T21:20:37.010084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q173
median149.66667
Q3288
95-th percentile732
Maximum74215
Range74214
Interquartile range (IQR)215

Descriptive statistics

Standard deviation1189.9481
Coefficient of variation (CV)4.5111873
Kurtosis2822.4916
Mean263.77715
Median Absolute Deviation (MAD)96.333333
Skewness49.088154
Sum1520147.7
Variance1415976.5
MonotonicityNot monotonic
2023-09-11T21:20:37.461215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 165
 
2.9%
2 72
 
1.2%
3 54
 
0.9%
4 51
 
0.9%
5 36
 
0.6%
6 28
 
0.5%
12 26
 
0.5%
100 21
 
0.4%
73 21
 
0.4%
72 20
 
0.3%
Other values (2361) 5269
91.4%
ValueCountFrequency (%)
1 165
2.9%
1.5 1
 
< 0.1%
2 72
1.2%
3 54
 
0.9%
3.333333333 1
 
< 0.1%
4 51
 
0.9%
5 36
 
0.6%
5.333333333 1
 
< 0.1%
5.666666667 1
 
< 0.1%
6 28
 
0.5%
ValueCountFrequency (%)
74215 1
< 0.1%
40498.5 1
< 0.1%
14149 1
< 0.1%
13956 1
< 0.1%
7824 1
< 0.1%
6009.333333 1
< 0.1%
5964 1
< 0.1%
5198 1
< 0.1%
4300 1
< 0.1%
4280 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

HIGH CORRELATION 

Distinct1170
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.933279
Minimum0.2
Maximum1110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-09-11T21:20:37.797223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1
Q17
median15
Q330.666667
95-th percentile173
Maximum1110
Range1109.8
Interquartile range (IQR)23.666667

Descriptive statistics

Standard deviation76.688683
Coefficient of variation (CV)2.0764114
Kurtosis33.17545
Mean36.933279
Median Absolute Deviation (MAD)10
Skewness5.0956999
Sum212846.49
Variance5881.1541
MonotonicityNot monotonic
2023-09-11T21:20:38.215410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 323
 
5.6%
2 167
 
2.9%
3 117
 
2.0%
7 106
 
1.8%
8 104
 
1.8%
5 104
 
1.8%
9 101
 
1.8%
10 100
 
1.7%
11 97
 
1.7%
6 95
 
1.6%
Other values (1160) 4449
77.2%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 7
0.1%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 11
0.2%
0.5454545455 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.6 1
 
< 0.1%
0.6176470588 1
 
< 0.1%
ValueCountFrequency (%)
1110 1
< 0.1%
749 1
< 0.1%
731 1
< 0.1%
721 1
< 0.1%
704 1
< 0.1%
687 1
< 0.1%
676 1
< 0.1%
674 1
< 0.1%
661 1
< 0.1%
650 1
< 0.1%

returns_qtt
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct218
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.72202
Minimum0
Maximum80995
Zeros4207
Zeros (%)73.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2023-09-11T21:20:38.777006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile39
Maximum80995
Range80995
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1461.3444
Coefficient of variation (CV)31.961502
Kurtosis2786.029
Mean45.72202
Median Absolute Deviation (MAD)0
Skewness52.290856
Sum263496
Variance2135527.5
MonotonicityNot monotonic
2023-09-11T21:20:39.197703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4207
73.0%
1 191
 
3.3%
2 158
 
2.7%
3 107
 
1.9%
4 90
 
1.6%
6 72
 
1.2%
5 64
 
1.1%
8 49
 
0.9%
12 49
 
0.9%
7 48
 
0.8%
Other values (208) 728
 
12.6%
ValueCountFrequency (%)
0 4207
73.0%
1 191
 
3.3%
2 158
 
2.7%
3 107
 
1.9%
4 90
 
1.6%
5 64
 
1.1%
6 72
 
1.2%
7 48
 
0.8%
8 49
 
0.9%
9 38
 
0.7%
ValueCountFrequency (%)
80995 1
< 0.1%
74215 1
< 0.1%
9014 1
< 0.1%
8060 1
< 0.1%
4627 1
< 0.1%
3768 1
< 0.1%
3335 1
< 0.1%
2975 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%

Interactions

2023-09-11T21:20:24.137141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:50.486514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:53.737663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:56.937662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:00.376801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:03.858215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:06.916110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:10.228866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:13.939414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:17.252668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:20.808701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:24.466663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:50.800093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:53.958539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:57.193846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:00.701014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:04.124089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:07.227252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:10.553675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:14.228380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:17.575424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:21.119816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:24.801377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:51.116448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:54.183286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:57.528102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:01.024470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:04.353321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:07.539516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:10.879998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:14.555846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:17.878532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:21.437662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:25.075271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:51.435634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:54.444531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:57.879464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:01.364753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:04.609220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:07.850885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:11.203518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:14.872373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:18.221053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:21.774338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:25.356928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:51.767515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:54.725918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:58.223252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:01.679304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:04.856668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:08.168216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:11.544685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:15.133555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:18.561311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:22.036077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:25.678264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:52.101138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:54.976948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:58.567473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:02.011178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:05.109900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:08.493211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:11.796168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:15.471660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:18.896689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:22.274683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:25.911608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:52.329787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:55.200764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:58.828814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:02.326244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:05.414563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:08.791493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:12.034240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:15.786390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:19.165862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:22.489668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:26.177300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:52.601206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:55.541232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:59.169092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:02.682369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:05.758260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:09.124867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:12.289871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:16.065602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:19.509311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:22.795503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:26.445072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:52.927824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:55.789221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:59.516401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:03.013601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:06.077338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:09.457065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:12.553307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:16.411524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:19.852069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:23.141080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:26.778545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:53.216901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:56.357446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:59.856560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:03.269854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:06.422615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:09.755871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:12.888297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:16.744369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:20.183541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:23.472137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:27.222123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:53.446351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:19:56.593377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:00.127119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:03.597797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:06.656750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:09.975519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:13.219762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:16.986257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:20.504293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-11T21:20:23.801364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-11T21:20:39.464313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
customer_idgross_revenueavg_ticketrecency_daysfrequencytotal_invoicesitems_qttproducts_qttavg_basket_sizeavg_unique_basket_sizereturns_qtt
customer_id1.000-0.172-0.3560.2450.387-0.396-0.307-0.072-0.1610.112-0.292
gross_revenue-0.1721.0000.349-0.406-0.4350.6260.9100.8370.7150.3900.431
avg_ticket-0.3560.3491.000-0.126-0.2020.2410.310-0.1280.260-0.3420.263
recency_days0.245-0.406-0.1261.0000.486-0.595-0.495-0.378-0.2010.044-0.326
frequency0.387-0.435-0.2020.4861.000-0.801-0.520-0.365-0.1400.090-0.373
total_invoices-0.3960.6260.241-0.595-0.8011.0000.6960.5370.168-0.1650.548
items_qtt-0.3070.9100.310-0.495-0.5200.6961.0000.7860.7950.3140.465
products_qtt-0.0720.837-0.128-0.378-0.3650.5370.7861.0000.6210.6630.321
avg_basket_size-0.1610.7150.260-0.201-0.1400.1680.7950.6211.0000.5810.191
avg_unique_basket_size0.1120.390-0.3420.0440.090-0.1650.3140.6630.5811.000-0.099
returns_qtt-0.2920.4310.263-0.326-0.3730.5480.4650.3210.191-0.0991.000

Missing values

2023-09-11T21:20:27.607321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-11T21:20:28.005901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenueavg_ticketrecency_daysfrequencytotal_invoicesitems_qttproducts_qttavg_basket_sizeavg_unique_basket_sizereturns_qtt
0178505391.2118.152222372.017.00000034.01733.0297.050.9705880.61764740.0
1130473237.5418.82290731.00.02915510.01391.0172.0139.10000010.60000036.0
2125837281.3829.4792712.00.04032315.05060.0247.0337.3333337.66666751.0
313748948.2533.86607195.00.0179215.0439.028.087.8000004.8000000.0
415100876.00292.000000333.00.0731713.080.03.026.6666670.33333322.0
5152914668.3045.32330125.00.04298015.02103.0103.0140.2000004.13333329.0
6146885630.8717.2197867.00.05722121.03621.0327.0172.4285717.047619399.0
7178095411.9188.71983616.00.03352012.02057.061.0171.4166673.83333342.0
81531160767.9025.5434640.00.24331691.038194.02379.0419.7142866.230769474.0
9145278508.828.7539302.00.14945755.02089.0972.037.9818186.00000040.0
customer_idgross_revenueavg_ticketrecency_daysfrequencytotal_invoicesitems_qttproducts_qttavg_basket_sizeavg_unique_basket_sizereturns_qtt
5936227004839.4278.0551611.01.01.01074.062.01074.055.00.0
593713298360.00180.0000001.01.01.096.02.096.02.00.0
593814569227.3918.9491671.01.01.079.012.079.010.00.0
59392270417.902.5571431.01.01.014.07.014.07.00.0
5940227053.351.6750001.01.01.02.02.02.02.00.0
5941227066637.5910.4528981.01.01.01748.0635.01748.0635.00.0
5942227077689.2310.5187820.01.01.02011.0731.02011.0731.00.0
5943227083217.2054.5288140.01.01.0654.059.0654.056.00.0
5944227095664.8925.9857340.01.01.0732.0218.0732.0218.00.0
594512713848.5522.3302630.01.01.0508.038.0508.038.00.0